Using Gaussian process based kernel classifiers for credit rating forecasting

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

The subprime mortgage crisis have triggered a significant economic decline over the world. Credit rating forecasting has been a critical issue in the global banking systems. The study trained a Gaussian process based multi-class classifier (GPC), a highly flexible probabilistic kernel machine, using variational Bayesian methods. GPC provides full predictive distributions and model selection simultaneously. During training process, the input features are automatically weighted by their relevances with respect to the output labels. Benefiting from the inherent feature scaling scheme, GPCs outperformed convectional multi-class classifiers and support vector machines (SVMs). In the second stage, conventional SVMs enhanced by feature selection and dimensionality reduction schemes were also compared with GPCs. Empirical results indicated that GPCs still performed the best.

Original languageEnglish
Pages (from-to)8607-8611
Number of pages5
JournalExpert Systems with Applications
Volume38
Issue number7
DOIs
Publication statusPublished - 2011 Jul 1

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Support vector machines
Classifiers
Feature extraction
Labels
Economics

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Using Gaussian process based kernel classifiers for credit rating forecasting. / Huang, Shian-Chang.

In: Expert Systems with Applications, Vol. 38, No. 7, 01.07.2011, p. 8607-8611.

Research output: Contribution to journalArticle

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